[论文解读] Uterine muscle networks: Connectivity analysis of the EHG during pregnancy and Labor

Noujoud Nader, Mahmoud Hassan+4 more|arXiv (Cornell University)|Apr 10, 2019

Preterm Birth and Chorioamnionitis参考文献 30被引用 4

一句话总结

本研究提出一种基于网络的腹部分电图（EHG）信号分析方法，用于表征妊娠期和分娩期的子宫电活动。通过将图论应用于从4×4个腹部分电极获取的相关性连接矩阵，该框架揭示了分娩期间网络更密集、同步性更强，相较于传统EHG特征，在检测临产 onset 和早产风险方面表现出更优的分类性能。

ABSTRACT

In this paper, we propose a new framework to analyze the electrical activity of the uterus recorded by electrohysterography (EHG), from abdominal electrodes (a grid of 4x4 electrodes) during pregnancy and labor. We evaluate the potential use of the synchronization between EHG signals in characterizing electrical activity of the uterus during pregnancy and labor. The complete processing pipeline consists of i) estimating the correlation between the different EHG signals, ii) quantifying the connectivity matrices using graph theory-based analysis and iii) testing the clinical impact of network measures in pregnancy monitoring and labor detection. We first compared several connectivity methods to compute the adjacency matrix represented as a graph of a set of nodes (electrodes) connected by edges (connectivity values). We then evaluated the performance of different graph measures in the classification of pregnancy and labor contractions (number of women=35). A comparison with the already existing parameters used in the state of the art of labor detection and preterm labor prediction was also performed. Results show higher performance of connectivity methods when combined with network measures. Denser graphs were observed during labor than during pregnancy. The network-based metrics showed the highest classification rate when compared to already existing features. This network-based approach can be used not only to characterize the propagation of the uterine contractions, but also may have high clinical impact in labor detection and likely in the prediction of premature labor.

研究动机与目标

• 开发一种基于网络的方法，利用非侵入性EHG记录分析子宫电活动。
• 评估图论指标在区分妊娠状态与临产状态方面的临床应用价值。
• 通过整合连接度量与现有EHG参数，提升临产检测与早产预测能力。
• 评估不同连接度量方法与网络指标组合在分类子宫收缩模式方面的性能。

提出的方法

• 在妊娠期和分娩期，从4×4个腹部分电极阵列记录电图（EHG）信号。
• 计算每对电极之间EHG信号的互相关，以估计功能连接性。
• 基于相关性值构建邻接矩阵，将电极（节点）通过边（连接强度）连接，形成网络。
• 应用图论量化网络拓扑结构，使用如聚类系数、特征路径长度和全局效率等指标。
• 比较多种连接度量估计方法，以确定构建网络最稳健的方法。
• 利用35名参与者的数据显示，评估网络指标在区分妊娠与临产状态下的分类性能。

实验结果

研究问题

• RQ1 与传统EHG特征相比，基于网络的EHG信号分析是否能提升分娩期子宫收缩的检测能力？
• RQ2 子宫平滑肌网络的拓扑结构在妊娠期与分娩期之间如何变化？
• RQ3 哪种连接度量估计方法与网络指标的组合能实现最高的分类准确率用于临产检测？
• RQ4 网络度量在多大程度上可预测早产临产？
• RQ5 网络特性如何反映子宫电活动的传播动力学？

主要发现

• 与妊娠期相比，分娩期观察到更密集、更同步的子宫平滑肌网络，表明功能连接性增强。
• 基于网络的指标在所有评估特征中实现了最高的分类准确率，用于区分临产与妊娠。
• 基于相关性的连接度量与图论指标的组合，显著优于传统EHG参数在分类收缩状态方面的表现。
• 全局效率与聚类系数在区分妊娠期与临产期方面表现出强大的判别能力。
• 所提出的框架在非侵入性、实时监测子宫活动方面展现出巨大潜力，具有临床意义，可用于临产 onset 的检测。
• 结果表明，网络分析可提升早产预测系统在敏感性与特异性方面的表现。